Speed Tracking Performance for a Coreless Linear Motor Servo System Based on a Fitted Adaptive Fuzzy Controller

Abstract

Fuzzy control is widely used in linear motor servo systems. However, simple fuzzy rules reduce the control accuracy of the servo system, while complex fuzzy rules reduce the speed of its decision making. This paper proposes a fitted adaptive fuzzy controller (FAFC) to improve the speed tracking performance of a coreless linear motor servo system. The FAFC took a planned curve as a motion target. The planned curve is compounded by multiple performance curves of fuzzy control under the same given input. These multiple performance curves cover the variation range of motor parameters, which are offline-obtained. The performance of the planned curve is ensured by the multiple offline performance curves. The FAFC only needs simple fuzzy rules to fit the planned curve, and achieves high control accuracy without affecting the decision speed. The experimental results verified the feasibility of an FAFC. This research shows that an FAFC can effectively shorten the online calculation time of complex algorithms and keep the consistency of performance.